Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning

• Main Authors: Jun Inoue, Yamagata, Yoriyuki, Yuqi Chen, Poskitt, Christopher M., Jun Sun
• Format: Conference Proceeding
• Language: English
• Subjects: Actuators; Anomaly detection; Computer architecture; deep neural network; machine learning; Monitoring; Sensors; support vector machine; Support vector machines; Time series analysis; water treatment system
• ISSN: 2375-9259
• DOI: 10.1109/ICDMW.2017.149

In this paper, we propose and evaluate the application of unsupervised machine learning to anomaly detection for a Cyber-Physical System (CPS). We compare two methods: Deep Neural Networks (DNN) adapted to time series data generated by a CPS, and one-class Support Vector Machines (SVM). These methods are evaluated against data from the Secure Water Treatment (SWaT) testbed, a scaled-down but fully operational raw water purification plant. For both methods, we first train detectors using a log generated by SWaT operating under normal conditions. Then, we evaluate the performance of both methods using a log generated by SWaT operating under 36 different attack scenarios. We find that our DNN generates fewer false positives than our one-class SVM while our SVM detects slightly more anomalies. Overall, our DNN has a slightly better F measure than our SVM. We discuss the characteristics of the DNN and one-class SVM used in this experiment, and compare the advantages and disadvantages of the two methods.